Building zip code models

I need to run analysis on activities happen in legislative districts level. One district often covers several zip code areas but part of a few of the zip code areas also belong to other legislative districts. For example, district A include zip code 90001, 90002, 90003, district B include zip code 90002, 90003, 90004, etc. When I construct single district model to run analysis, should I include data of all the zip codes that all or partly fall into the district? In the above example, if I use data 90002 and 90003 in both district A and district B models, is there a potential of double counting? I can separate the final demand change in the two districts pretty well though. Thank you for your advice!

Comments

We do offer a CD package that has congressional districts within a county. Not sure if that would help for your purposes, as you are working with legislative districts but I wanted to be sure I let you know.

A conservative option would be to model the impacts on two completely independent study regions with no overlap, defining these study regions/legislative districts using the zip-code(s) based on whichever legislative district contains the majority of the zip-code.

Alternatively, since you have enough information to separate the final demand into the two districts, you could model the two final demands or Direct Effects in the distinct districts they apply to, but have each district defined as all the zip-codes that fall into each district as you described (district A including zip code 90001, 90002, 90003, and district B including zip code 90002, 90003, 90004). If you are sure to not create any double counting in your modeled inputs or Direct Effects, then you'll only potentially have some overstated Indirect and Induced Effects since the RPCs for some commodities will be higher than in reality by including the entire zip code where only part of it should ideally be included.

For example: Suppose that the zip-code includes the only veterinarian in the state. If that zip-code is included in both study areas, then the RPC for veterinary services will be > 0 in both study areas (and thus there will be indirect and induced impacts on the veterinary services sector), when in reality we know that that veterinarian really only belongs in one of those two study areas such that one of those study areas should have an RPC of 0 for veterinary services (and thus 0 indirect or induced impacts in that sector). However, it works the other way too: Having that veterinarian in both places causes greater demand in both areas for things that veterinarians need, like medical supplies, and that increased demand for those things reduces the RPCs for those things, thereby possibly dampening some impacts that should otherwise be larger since the RPC for them should be larger.